System and methods for detecting vehicle braking events using data from fused sensors in mobile devices

• US 10,112,530 B1
• Filed: 09/20/2017
• Issued: 10/30/2018
• Est. Priority Date: 12/28/2016
• Status: Active Grant

• Current Assignee: Arity International Limited (Allstate Corporation)
• Original Assignee: Allstate Insurance Company (Allstate Corporation)
• Inventors: Snyder, Jared S., Ferguson, Dana
• Primary Examiner(s): Singh, Hirdepal
• Application Number:
  US15/709,889
• Time in Patent Office: 405 Days
• Field of Search: 340903
• CPC Class Codes: B60T 17/22 Devices for monitoring or checking brake systems; Signal devices
• CPC Class Codes: B60W 2040/0818 Inactivity or incapacity of driver
• CPC Class Codes: B60W 2040/0863 due to erroneous selection or response of the driver
• CPC Class Codes: B60W 2540/22 Psychological state; Stress level or workload
• CPC Class Codes: B60W 2540/229 Attention level, e.g. attentive to driving, reading or sleeping
• CPC Class Codes: B60W 2540/24 Drug level, e.g. alcohol
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

One or more braking event detection computing devices and methods are disclosed herein based on fused sensor data collected during a window of time from various sensors of a mobile device found within an interior of a vehicle. The various sensors of the mobile device may include a GPS receiver, an accelerometer, a gyroscope, a microphone, a camera, and a magnetometer. Data from vehicle sensors and other external systems may also be used. The braking event detection computing devices may adjust the polling frequency of the GPS receiver of the mobile device to capture non-consecutive data points based on the speed of the vehicle, the battery status of the mobile device, traffic-related information, and weather-related information. The braking event detection computing devices may use classification machine learning algorithms on the fused sensor data to determine whether or not to classify a window of time as a braking event.

Claims

1. A system comprising:

one or more processors; and

memory storing computer-readable instructions that, when executed by the one or more processors, cause the system to;

collect, by a sensor data collection device of the system, sensor data from sensors associated with a computing device within a vehicle during a first window of time and using a polling frequency, wherein the sensor data comprises information relating to at least a location, a speed, and an acceleration of the vehicle;

process, by a sensor data processing device of the system, the sensor data, wherein processing comprises removing one or more duplicate data points from the sensor data and generating processed sensor data;

determine, by a braking event classification device of the system and based on the sensor data and the processed sensor data, to classify a second window of time as a braking event; and

generate and transmit, by a braking event notification device of the system, a notification relating to the braking event of the vehicle to at least one of;

a second computing device within a first predetermined distance of the vehicle or a second vehicle within a second predetermined distance of the vehicle.

2. The system of claim 1, wherein processing the sensor data further comprises:

replacing, for each respective duplicate data point of the one or more duplicate data points, the respective duplicate data point with an average value of a first data point and a second data point, wherein the first data point immediately precedes the respective duplicate data point, and wherein the second data point immediately follows the respective duplicate data point.

3. The system of claim 1, further including instructions that, when executed by the one or more processors, cause the system to:

perform, by a sensor data calibration device of the system, an alignment of a first axis, a second axis, and a third axis of the sensor data with an x-axis, a y-axis, and a z-axis of a reference frame of the vehicle.

4. The system of claim 1, further including instructions that, when executed by the one or more processors, cause the system to:

collect, by the sensor data collection device, supplemental sensor data from sensors associated with the vehicle during the first window of time; and

process, by the sensor data processing device, the supplemental sensor data, wherein processing the supplemental sensor data comprises removing duplicate data points and generating processed supplemental sensor data, and wherein determining to classify the second window of time as the braking event is further based on the processed supplemental sensor data.

5. The system of claim 1, further including instructions that, when executed by the one or more processors, cause the system to:

apply, by the braking event classification device and based on the sensor data and the processed sensor data, a classification machine learning algorithm to determine a probability that the second window of time relates to the braking event, wherein determining to classify the second window of time as the braking event is further based on determining that the probability meets a threshold.

6. The system of claim 1, wherein the notification is transmitted via a short-range communication protocol.

7. The system of claim 1, wherein determining to classify the second window of time as the braking event is further based on a random forest machine learning algorithm.

8. A method comprising:

collecting, by a sensor data collecting device of a system, sensor data from sensors associated with a computing device within a vehicle during a first window of time and using a polling frequency, wherein the sensor data comprises information relating to at least a location, a speed, and an acceleration of the vehicle;

processing, by a sensor data processing device of the system, the sensor data, wherein processing comprises removing duplicate data points and generating processed sensor data;

determining, by a braking event classification device of the system and based on the sensor data and processed sensor data, to classify a second window of time as a braking event; and

generating and transmitting, by a braking event notification device of the system, a notification relating to the braking event of the vehicle to at least one of;

a second computing device within a first predetermined distance of the vehicle and a second vehicle within a second predetermined distance of the vehicle.

9. The method of claim 8, further comprising:

replacing, for each respective duplicate data point of the one or more duplicate data points, the respective duplicate data point with an average value of a first data point and a second data point, wherein the first data point immediately precedes the respective duplicate data point, and wherein the second data point immediately follows the respective duplicate data point.

10. The method of claim 8, further comprising:

performing an alignment of a first axis, a second axis, and a third axis of the sensor data with an x-axis, a y-axis, and a z-axis of a reference frame of the vehicle.

11. The method of claim 8, further comprising:

collecting, by the sensor data collection device, supplemental sensor data from sensors associated with the vehicle during the first window of time; and

processing, by the sensor data processing device, the supplemental sensor data, wherein processing the supplemental sensor data comprises removing duplicate data points and generating processed supplemental sensor data, and wherein determining to classify the second window of time as the braking event is further based on the processed supplemental sensor data.

12. The method of claim 8, further comprising:

applying, by the braking event classification device and based on the sensor data and processed sensor data, a classification machine learning algorithm to determine a probability that the second window of time relates to the braking event, wherein determining to classify the second window of time as the braking event is further based on determining that the probability meets a threshold.

13. The method of claim 8, wherein the notification is transmitted via a short-range communication protocol.

14. The method of claim 8, wherein determining to classify the second window of time as the braking event is further based on a classification machine learning algorithm.

15. A computer-assisted method of detecting braking events comprising:

collecting, by a sensor data collecting device of a system, sensor data from sensors associated with a computing device within a vehicle during a first window of time and using a polling frequency, wherein the sensor data comprises information relating to at least a location, a speed, and an acceleration of the vehicle;

processing, by a sensor data processing device of the system, the sensor data, wherein processing comprises replacing duplicate data points with one or more average data points, and wherein the one or more average data points are based on one or more values of data points near the duplicate data points;

determining, by a braking event classification device and based on the sensor data and processed sensor data, to classify a second window of time as a braking event; and

generating and transmitting, by a braking event notification device of the system, a visual notification to at least one of;

a second computing device within a first predetermined distance of the vehicle and a second vehicle within a second predetermined distance of the vehicle relating to the braking event of the vehicle.

16. The computer-assisted method of claim 15, further comprising:

performing an alignment of a first axis, a second axis, and a third axis of the sensor data with an x-axis, a y-axis, and a z-axis of a reference frame of the vehicle.

17. The computer-assisted method of claim 15, further comprising:

collecting, by the sensor data collecting device, supplemental sensor data from sensors associated with the vehicle during the first window of time; and

processing, by the sensor data processing device, the supplemental sensor data, wherein processing the supplemental sensor data comprises removing duplicate data points and generating processed supplemental sensor data, and wherein determining to classify the second window of time as the braking event is further based on the processed supplemental sensor data.

18. The computer-assisted method of claim 15, further comprising:

applying, by the braking event classification device and based on the sensor data and processed sensor data, a classification machine learning algorithm to determine a probability that the sensor data associated with the second window of time is the braking event, wherein determining to classify the second window of time as the braking event is further based on determining that the probability meets a threshold.

19. The computer-assisted method of claim 15, further comprising:

using short-range communication protocols to send the visual notification to nearby vehicles.

20. The computer-assisted method of claim 15, wherein determining to classify the second window of time as the braking event is further based on a classification machine learning algorithm.